Wine Classification - Neural Network

rm(list=ls())

Libraries

library(tidyverse)
library(neuralnet)
library(caret)
library(NeuralNetTools)
library(janitor)
library(kableExtra)

Reading, cleaning and preparing the data

df <- read.csv("wine_data2.csv")

Unselecting columns

df <- df %>%
  select(-c("X","Unnamed._0")) %>%
  janitor::clean_names() %>%
  mutate_if(is.character,as.factor)

Scaling numerical data

df[,c(1:12)] <- sapply(df[,c(1:12)], function(x) scale(x))

levels(df$type)

## [1] "red"   "white"

Adding boolean columns for each wine type

df$red <- df$type == "red"
df$white <- df$type == "white"

df %>%
  kable(align = "c") %>%
  kable_styling(position="center") %>%
  scroll_box(height='500px',width="100%")

fixed_acidity	volatile_acidity	citric_acid	residual_sugar	chlorides	free_sulfur_dioxide	total_sulfur_dioxide	density	p_h	sulphates	alcohol	quality	type	red	white
0.1424623	2.1886645	-2.1926638	-0.7447208	0.5699140	-1.1000552	-1.4462472	1.0349132	1.8129500	0.1930819	-0.9153937	-0.9371575	red	TRUE	FALSE
0.4510010	3.2819823	-2.1926638	-0.5975941	1.1978825	-0.3112961	-0.8624022	0.7014323	-0.1150642	0.9995017	-0.5800235	-0.9371575	red	TRUE	FALSE
0.4510010	2.5531038	-1.9174051	-0.6606484	1.0266184	-0.8746955	-1.0924018	0.7681285	0.2580999	0.7978967	-0.5800235	-0.9371575	red	TRUE	FALSE
3.0735801	-0.3624106	1.6609574	-0.7447208	0.5413699	-0.7620156	-0.9862481	1.1016093	-0.3638402	0.3274852	-0.5800235	0.2079830	red	TRUE	FALSE
0.1424623	2.1886645	-2.1926638	-0.7447208	0.5699140	-1.1000552	-1.4462472	1.0349132	1.8129500	0.1930819	-0.9153937	-0.9371575	red	TRUE	FALSE
0.1424623	1.9457049	-2.1926638	-0.7657389	0.5413699	-0.9873753	-1.3400936	1.0349132	1.8129500	0.1930819	-0.9153937	-0.9371575	red	TRUE	FALSE
0.5281357	1.5812656	-1.7797758	-0.8077751	0.3701058	-0.8746955	-1.0039404	0.5680400	0.5068759	-0.4789346	-0.9153937	-0.9371575	red	TRUE	FALSE
0.0653276	1.8849651	-2.1926638	-0.8918475	0.2559297	-0.8746955	-1.6762467	-0.0322255	1.0666219	-0.4117329	-0.4123384	1.3531236	red	TRUE	FALSE
0.4510010	1.4597859	-2.0550344	-0.7237027	0.4842819	-1.2127351	-1.7293236	0.7014323	0.8800399	0.2602836	-0.8315512	1.3531236	red	TRUE	FALSE
0.2195970	0.9738668	0.2846641	0.1380395	0.4271938	-0.7620156	-0.2431727	1.0349132	0.8178459	1.8059215	0.0068744	-0.9371575	red	TRUE	FALSE
-0.3974804	1.4597859	-1.6421464	-0.7657389	1.1693385	-0.8746955	-0.8977868	0.4012996	0.3824879	0.0586786	-1.0830788	-0.9371575	red	TRUE	FALSE
0.2195970	0.9738668	0.2846641	0.1380395	0.4271938	-0.7620156	-0.2431727	1.0349132	0.8178459	1.8059215	0.0068744	-0.9371575	red	TRUE	FALSE
-1.2459619	1.6723755	-2.1926638	-0.8077751	0.9409863	-0.8183555	-1.0039404	-0.1322698	2.2483080	-0.0757247	-0.4961809	-0.9371575	red	TRUE	FALSE
0.4510010	1.6420055	-0.1970385	-0.8077751	1.6545869	-1.2127351	-1.5347086	0.9015208	0.2580999	6.9132468	-1.1669214	-0.9371575	red	TRUE	FALSE
1.2994825	1.7027454	-0.9539998	-0.3453769	3.4243165	1.2098821	0.5175949	1.3016978	-0.3638402	2.3435347	-1.0830788	-0.9371575	red	TRUE	FALSE
1.2994825	1.7027454	-0.8851851	-0.3243588	3.2530523	1.1535421	0.5706717	1.3016978	-0.3016462	2.6795429	-1.0830788	-0.9371575	red	TRUE	FALSE
0.9909438	-0.3624106	1.6609574	-0.7657389	1.0266184	0.2521032	-0.2254805	0.7347804	0.5068759	1.4699132	0.0068744	1.3531236	red	TRUE	FALSE
0.6824050	1.3383061	-0.2658532	-0.7867570	8.9047693	-0.8183555	-1.0570172	0.7014323	-0.6748102	5.0316006	-0.9992363	-0.9371575	red	TRUE	FALSE
0.1424623	1.5205258	-1.6421464	-0.2192683	0.8553542	-1.3817549	-1.5347086	0.9015208	1.0044279	-0.2101280	-1.2507639	-2.0822980	red	TRUE	FALSE
0.5281357	-0.1194510	1.3168841	-0.7657389	8.1340806	-0.7620156	-1.0570172	0.7347804	-1.1101682	3.6875676	-1.0830788	0.2079830	red	TRUE	FALSE
1.2994825	-0.7268499	1.1104401	-0.7657389	0.5984580	-0.0859364	-0.9862481	0.7014323	1.0666219	-0.0085230	-0.9153937	0.2079830	red	TRUE	FALSE
0.2967317	0.3057281	-0.0594092	-0.6606484	0.7411781	-0.4239760	-0.7916332	1.1683055	1.8751440	0.7978967	-0.6638660	-0.9371575	red	TRUE	FALSE
0.5281357	0.5486877	-0.7475558	-0.8077751	1.4262347	-1.1563951	-1.3931704	0.6347362	-0.3016462	2.5451396	-0.8315512	-0.9371575	red	TRUE	FALSE
0.9909438	0.9131269	-1.4357025	-0.6606484	0.7982662	-1.2127351	-0.8624022	0.7014323	-0.3016462	-0.0085230	-0.9153937	-0.9371575	red	TRUE	FALSE
-0.2432111	0.3664680	-1.2292585	-0.6396303	0.8268102	-0.5366559	-1.3400936	0.7014323	1.3153979	0.6634934	-0.6638660	0.2079830	red	TRUE	FALSE
-0.7060192	0.3057281	-1.0916291	-0.8498113	0.6840901	-1.1000552	-1.6408622	0.2679072	0.7556519	0.1930819	-0.9992363	-0.9371575	red	TRUE	FALSE
0.2967317	0.4272079	-0.5411118	-0.7657389	0.6840901	-1.4944347	-1.8531695	0.5013438	0.3824879	0.3946869	-0.8315512	-0.9371575	red	TRUE	FALSE
0.5281357	0.5486877	-0.7475558	-0.8077751	1.4262347	-1.1563951	-1.3931704	0.6347362	-0.3016462	2.5451396	-0.8315512	-0.9371575	red	TRUE	FALSE
-0.0889417	2.2494043	-2.1926638	-0.7447208	0.6840901	-0.9310354	-1.4285549	0.8348247	1.5641740	0.1258803	-0.9153937	-0.9371575	red	TRUE	FALSE
0.4510010	1.8545951	-2.1926638	-0.7237027	0.7411781	-1.2690750	-1.7647081	0.5680400	1.0044279	0.3946869	-0.5800235	0.2079830	red	TRUE	FALSE
-0.3974804	2.0368148	-1.7109611	-0.6396303	0.9409863	-0.7620156	-0.5970182	0.3679515	0.8178459	0.0586786	-0.3284958	-0.9371575	red	TRUE	FALSE
-0.2432111	2.0975546	-2.1926638	-0.6186122	1.3976907	-0.4803159	-1.3931704	0.6347362	1.5019799	0.2602836	0.0907169	0.2079830	red	TRUE	FALSE
0.8366744	1.9153350	-1.3668878	-0.6606484	0.7697221	-0.8746955	-0.0485578	0.6347362	-0.3016462	0.8650984	-0.5800235	-0.9371575	red	TRUE	FALSE
-0.2432111	1.6116356	-1.3668878	1.1048721	0.4842819	0.5338029	-0.5793259	1.5351344	1.4397859	-0.0757247	-0.9153937	0.2079830	red	TRUE	FALSE
-1.5545006	-0.1194510	-0.4722972	-0.7657389	1.3406026	-0.9873753	-1.1631709	0.3346034	1.0044279	0.1258803	-1.0830788	-0.9371575	red	TRUE	FALSE
0.4510010	1.8545951	-2.1926638	0.0119309	0.8553542	-1.4380948	-1.7293236	1.3016978	1.1288159	0.1258803	-0.7477086	0.2079830	red	TRUE	FALSE
0.4510010	1.5812656	-1.2292585	-0.6396303	0.8553542	-1.5507747	-1.7824004	0.9348689	1.2532039	0.4618885	0.2584021	0.2079830	red	TRUE	FALSE
0.6824050	0.2449882	-0.2658532	-0.7026846	0.2844737	-0.9873753	-1.5170163	0.7014323	0.0715178	1.3355099	-0.6638660	1.3531236	red	TRUE	FALSE
-1.1688272	4.8004794	-1.5733318	-0.8287932	3.3101404	-1.3254149	-1.7116313	-0.2323140	1.7507560	-0.3445313	-0.5800235	-2.0822980	red	TRUE	FALSE
0.0653276	0.6701674	0.2846641	0.0960033	0.5128259	-1.0437153	-0.5085568	1.0349132	0.6934579	2.0075264	0.0068744	-0.9371575	red	TRUE	FALSE
0.0653276	0.6701674	0.2846641	0.0960033	0.5128259	-1.0437153	-0.5085568	1.0349132	0.6934579	2.0075264	0.0068744	-0.9371575	red	TRUE	FALSE
1.2223478	1.6420055	-0.1282238	-0.5555579	0.9124423	-0.7620156	-1.2339400	0.9682170	0.2580999	-0.1429263	-0.9992363	-2.0822980	red	TRUE	FALSE
0.2195970	0.9131269	-0.8163705	-0.5975941	7.8771844	-1.2690750	-1.8000926	0.7014323	-0.0528702	2.4779380	0.0068744	0.2079830	red	TRUE	FALSE
0.6824050	1.9457049	-0.6787412	-0.6816665	0.3701058	-1.2127351	-1.6408622	0.7014323	0.5068759	4.4939874	-0.1608107	-0.9371575	red	TRUE	FALSE
-0.3203458	2.0064448	-2.0550344	-0.7657389	-0.1722307	-1.4380948	-1.8531695	0.5013438	1.6263680	-0.0757247	-0.8315512	-0.9371575	red	TRUE	FALSE
-2.0173087	1.0953466	-1.1604438	-0.7026846	-0.0580546	-1.2690750	-0.8977868	-0.4324025	4.2385162	0.1930819	2.1867808	-2.0822980	red	TRUE	FALSE
0.3738663	3.6160517	0.7663668	-0.6816665	1.6545869	-0.4803159	-0.0308655	0.7681285	0.1959058	1.3355099	-1.0830788	-0.9371575	red	TRUE	FALSE
1.1452131	-0.3016707	1.3856988	-0.8077751	1.6260429	-1.0437153	-1.3931704	0.7347804	0.1959058	0.3274852	-0.8315512	-0.9371575	red	TRUE	FALSE
-0.6288845	0.3664680	-0.6099265	-0.8077751	0.2844737	-1.4380948	-1.8354772	0.3679515	0.7556519	0.1930819	-1.0830788	-0.9371575	red	TRUE	FALSE
-1.2459619	-0.1801909	0.3534788	-0.8498113	0.5128259	-1.0437153	-0.3493264	0.2345591	0.6312639	0.3274852	-1.0830788	-0.9371575	red	TRUE	FALSE
1.2223478	1.9457049	-0.4034825	-0.7867570	0.5128259	-1.4944347	-1.6408622	0.8014766	-0.4260342	1.4027116	-1.0830788	-0.9371575	red	TRUE	FALSE
-0.4746151	1.0953466	-1.9174051	-0.6816665	0.3701058	-1.2690750	-1.7824004	0.3012553	1.1288159	0.6634934	-0.9153937	0.2079830	red	TRUE	FALSE
-0.4746151	0.9738668	-1.9174051	-0.7026846	0.3415618	-1.3817549	-1.8000926	0.2679072	1.0666219	0.7306951	-0.9153937	0.2079830	red	TRUE	FALSE
1.0680784	0.2449882	0.2846641	-0.5135217	0.7126341	-0.0295965	0.0575959	0.7681285	-0.1150642	0.1930819	-0.9153937	-0.9371575	red	TRUE	FALSE
0.2967317	1.0346067	-1.1604438	-0.5555579	1.5404108	0.1394233	-0.7562486	0.2679072	-0.3016462	0.6634934	-0.2446533	0.2079830	red	TRUE	FALSE
0.3738663	1.7027454	-1.9174051	-0.3453769	0.7982662	-0.3112961	-1.2516322	1.0349132	0.7556519	-0.0085230	-0.8315512	-0.9371575	red	TRUE	FALSE
2.3022333	0.4879478	1.7297721	-0.4294493	0.3986498	-1.4944347	-1.8708617	0.8014766	-1.1101682	0.6634934	-0.7477086	-0.9371575	red	TRUE	FALSE
0.2195970	1.7634853	-1.3668878	-0.0721416	1.5689548	1.0972022	-0.1016346	1.2016536	0.2580999	1.6043165	-0.9153937	-0.9371575	red	TRUE	FALSE
0.4510010	1.5205258	-0.9539998	-0.6606484	0.5699140	-0.7620156	-1.0924018	0.9348689	1.3153979	0.3946869	-0.4123384	-0.9371575	red	TRUE	FALSE
0.0653276	0.3057281	-0.0594092	-0.6396303	0.5128259	-1.2127351	-1.2339400	0.5013438	1.1910099	0.0586786	-0.9153937	0.2079830	red	TRUE	FALSE
1.2223478	0.3664680	0.5599228	-0.6816665	0.6555460	-0.6493357	-1.1277863	1.1016093	1.3775919	0.7306951	-1.0830788	-0.9371575	red	TRUE	FALSE
0.3738663	2.1279246	1.1792548	-0.7657389	1.6831309	-0.5929958	-0.0662500	0.7014323	-0.0528702	1.2011066	-0.9992363	-0.9371575	red	TRUE	FALSE
0.2195970	1.0953466	-1.0916291	-0.7447208	0.8268102	-1.0437153	-1.4285549	0.7014323	1.0044279	0.5962918	-0.8315512	1.3531236	red	TRUE	FALSE
-0.1660764	2.4012541	-1.8485904	-0.7237027	0.7126341	-0.9873753	-1.0924018	0.6347362	1.0666219	0.2602836	-0.5800235	-0.9371575	red	TRUE	FALSE
-0.0118071	2.3405142	-1.8485904	-0.1667230	0.8553542	-1.4944347	-1.8531695	0.5013438	1.1910099	-0.9493461	0.3422446	-0.9371575	red	TRUE	FALSE
-0.0118071	2.3405142	-1.8485904	-0.1667230	0.8553542	-1.4944347	-1.8531695	0.5013438	1.1910099	-0.9493461	0.3422446	-0.9371575	red	TRUE	FALSE
0.2195970	1.0953466	-1.4357025	-0.8287932	0.6555460	-1.1000552	-1.3577859	0.7014323	1.2532039	0.3274852	-0.7477086	-0.9371575	red	TRUE	FALSE
-0.4746151	2.2190344	-1.7109611	-0.8077751	0.5699140	-1.3817549	-1.7824004	0.5013438	1.3775919	0.3274852	0.1745595	-0.9371575	red	TRUE	FALSE
1.6080212	-0.1194510	1.7297721	-0.7237027	0.5128259	-0.1986163	-0.8977868	0.7347804	0.3824879	1.7387198	0.1745595	-0.9371575	red	TRUE	FALSE
0.6052704	2.2190344	-1.8485904	-0.7447208	0.5128259	-1.2690750	-1.7116313	0.5013438	0.7556519	2.8139462	0.0068744	0.2079830	red	TRUE	FALSE
0.3738663	1.7634853	-1.6421464	-0.7447208	0.5699140	-0.8746955	-1.5700931	0.6680842	0.6312639	0.0586786	-0.8315512	0.2079830	red	TRUE	FALSE
0.3738663	2.0064448	-0.6099265	-0.7026846	0.9124423	-0.7620156	-0.3493264	0.5013438	0.6312639	-0.3445313	-0.8315512	-0.9371575	red	TRUE	FALSE
0.3738663	2.1279246	-0.6787412	-0.7447208	0.7982662	-0.7056757	-0.3847109	0.4679957	0.5690699	-0.3445313	-0.8315512	-0.9371575	red	TRUE	FALSE
0.8366744	2.0368148	-0.4034825	-0.7026846	0.7982662	-1.1000552	-1.2870168	0.9682170	0.5690699	-0.0085230	-1.0830788	-2.0822980	red	TRUE	FALSE
1.9165599	-0.1194510	1.5233281	-0.6186122	1.0837064	-0.1422763	-0.5793259	1.2350017	0.3824879	1.9403248	-0.7477086	-0.9371575	red	TRUE	FALSE
1.2223478	0.4272079	2.2114747	-0.6816665	1.0551624	-1.2127351	-1.3047090	1.3016978	1.9995320	0.8650984	0.0068744	-0.9371575	red	TRUE	FALSE
1.2223478	0.4272079	2.2114747	-0.6816665	1.0551624	-1.2127351	-1.3047090	1.3016978	1.9995320	0.8650984	0.0068744	-0.9371575	red	TRUE	FALSE
-0.3203458	2.7049535	-2.1926638	-0.6396303	1.3691467	-0.9310354	-1.5170163	0.6347362	1.8751440	0.1258803	0.1745595	0.2079830	red	TRUE	FALSE
-0.3974804	2.4923639	-1.3668878	-0.7237027	0.8553542	-1.0437153	-0.6324027	0.3679515	1.0044279	-0.0757247	-0.3284958	-0.9371575	red	TRUE	FALSE
0.8366744	1.7331153	-0.8163705	-0.8287932	0.6840901	-0.1986163	0.0575959	0.8348247	-0.3638402	3.9563742	-1.1669214	-2.0822980	red	TRUE	FALSE
-0.7831538	0.6701674	-0.8163705	-0.8077751	0.3701058	-1.5507747	-1.7824004	0.3679515	1.1910099	0.1930819	-1.0830788	-0.9371575	red	TRUE	FALSE
0.4510010	0.5486877	2.6243627	-0.7447208	11.6449957	-0.4803159	-0.8624022	0.9015208	-0.5504222	5.0316006	-0.9153937	-0.9371575	red	TRUE	FALSE
0.1424623	0.9738668	1.0416254	-0.7237027	0.8553542	-0.5366559	-0.7562486	0.7681285	0.8800399	0.2602836	-1.1669214	-0.9371575	red	TRUE	FALSE
0.0653276	2.0064448	-0.4034825	-0.7657389	9.8467221	-0.8183555	-1.1454786	0.7347804	-0.3638402	4.0907775	-0.9153937	-0.9371575	red	TRUE	FALSE
-0.7060192	-0.2409308	1.1104401	-0.7657389	0.3701058	-0.7056757	-0.9685559	0.4012996	1.3775919	1.6715182	-0.1608107	0.2079830	red	TRUE	FALSE
-0.2432111	1.2775662	-1.1604438	-0.6816665	0.5699140	-0.6493357	-1.3400936	0.4679957	1.1910099	0.3946869	-0.3284958	-0.9371575	red	TRUE	FALSE
1.0680784	0.9131269	-0.2658532	-0.7447208	1.5404108	-0.5929958	0.3583645	0.8348247	-1.7943023	9.5341110	-0.4961809	0.2079830	red	TRUE	FALSE
0.3738663	0.9131269	-0.4034825	-0.7447208	0.1702976	-1.2127351	-1.4993240	0.6347362	1.0666219	0.7306951	-0.7477086	-0.9371575	red	TRUE	FALSE
1.6080212	0.3057281	0.8351814	-0.7026846	1.4547787	0.1957633	0.1637495	1.0349132	-0.4882282	4.6283907	-0.8315512	-0.9371575	red	TRUE	FALSE
-0.1660764	1.7027454	-1.6421464	-0.7657389	0.5699140	-1.2690750	-1.6231699	1.0349132	1.6263680	-0.0085230	-1.2507639	-0.9371575	red	TRUE	FALSE
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2.6879067	0.0627686	1.4545134	-0.5975941	0.3986498	-1.4380948	-1.7647081	0.8348247	-0.4260342	0.7978967	0.4260872	2.4982641	red	TRUE	FALSE
0.4510010	1.0953466	-0.4722972	-0.7447208	0.7126341	-0.9310354	-1.3754781	1.2350017	1.3153979	0.7978967	-1.2507639	0.2079830	red	TRUE	FALSE
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2.6879067	0.0627686	1.4545134	-0.5975941	0.3986498	-1.4380948	-1.7647081	0.8348247	-0.4260342	0.7978967	0.4260872	2.4982641	red	TRUE	FALSE
1.1452131	2.1279246	-0.0594092	-0.5135217	0.8553542	-0.4239760	-0.6147104	1.8352672	1.6263680	1.4027116	0.9291425	0.2079830	red	TRUE	FALSE
0.4510010	1.0953466	-0.4722972	-0.7447208	0.7126341	-0.9310354	-1.3754781	1.2350017	1.3153979	0.7978967	-1.2507639	0.2079830	red	TRUE	FALSE
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0.7595397	0.4879478	1.1792548	-0.5975941	0.7982662	0.0830834	-1.0747095	1.3683940	0.7556519	1.4699132	-1.5022916	0.2079830	red	TRUE	FALSE
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3.6135228	0.2449882	1.1792548	-0.5765760	1.1978825	-1.0437153	-1.3047090	1.9019634	-0.3638402	0.5290902	-0.1608107	-0.9371575	red	TRUE	FALSE
3.6135228	0.2449882	1.1792548	-0.5765760	1.1978825	-1.0437153	-1.3047090	1.9019634	-0.3638402	0.5290902	-0.1608107	-0.9371575	red	TRUE	FALSE
2.3793680	-0.4231505	-0.5411118	-0.7026846	0.4557379	-0.8746955	-1.4639395	0.3012553	0.0093238	0.8650984	1.9352531	0.2079830	red	TRUE	FALSE
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0.6824050	2.9479130	-0.5411118	-0.7026846	0.7982662	-1.4380948	-1.8177849	0.8348247	0.9422339	1.6043165	0.1745595	0.2079830	red	TRUE	FALSE
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3.0735801	1.9457049	-0.5411118	-0.6186122	0.8268102	-0.8183555	-1.1100941	1.5351344	-0.9857802	1.2683083	0.4260872	0.2079830	red	TRUE	FALSE
5.4647551	-0.1801909	2.8996214	-0.7657389	0.5413699	-1.3817549	-1.7824004	2.0353557	-2.2296603	1.7387198	-1.7538193	0.2079830	red	TRUE	FALSE
1.4537518	0.7916472	1.1792548	-0.5975941	1.0837064	0.4211230	-0.1724037	1.1683055	-0.8613922	0.3946869	-1.1669214	-0.9371575	red	TRUE	FALSE
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6.3903712	1.8545951	1.1792548	-0.2613045	1.1122504	-1.1563951	-1.6408622	2.8190357	-1.8564963	1.4027116	0.5099297	-0.9371575	red	TRUE	FALSE
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6.4675059	1.8545951	1.1792548	-0.2613045	1.1122504	-1.1563951	-1.6408622	2.8190357	-1.8564963	1.4027116	0.5099297	-0.9371575	red	TRUE	FALSE
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3.4592534	0.9131269	1.1792548	-0.6816665	0.7697221	-1.4380948	-1.7824004	1.7685710	-0.1772582	-0.6805395	-1.0830788	-0.9371575	red	TRUE	FALSE
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3.5363881	-0.0587112	1.1792548	-0.4294493	1.0551624	1.3225619	-0.6324027	1.8352672	0.5068759	1.5371149	0.1745595	1.3531236	red	TRUE	FALSE
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2.9964454	-0.1801909	1.1792548	-0.5765760	1.0837064	-0.8183555	-1.2162477	1.3016978	-0.6126162	3.2843577	0.0907169	1.3531236	red	TRUE	FALSE
0.0653276	2.3708841	-0.5411118	-0.7447208	1.4833228	-0.7056757	-0.2431727	0.6680842	0.2580999	0.3946869	-0.9992363	-0.9371575	red	TRUE	FALSE
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0.5281357	2.3101442	-1.0228145	-0.5975941	1.1407945	-0.5929958	-1.3754781	1.0349132	1.1288159	-0.0085230	-0.8315512	-0.9371575	red	TRUE	FALSE
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4.6162736	0.7309073	1.3856988	-0.6816665	0.4271938	-1.0437153	-1.4285549	1.9686595	-0.7370042	0.1930819	-1.2507639	0.2079830	red	TRUE	FALSE
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-0.7831538	0.1235085	0.0094055	-0.3033407	-0.5718471	0.7591626	-0.4200955	-1.7796651	-0.1150642	-0.2101280	2.3544659	1.3531236	white	FALSE	TRUE
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-0.7831538	0.0931385	0.2158495	-0.7237027	-0.2864068	0.0267435	-0.3670186	-2.1598333	-0.9857802	-0.4789346	2.6059936	0.2079830	white	FALSE	TRUE
-1.1688272	0.4272079	-0.7475558	-0.7447208	-0.2293188	-0.0295965	-0.0662500	-1.1060338	0.4446819	0.1258803	0.5937723	0.2079830	white	FALSE	TRUE
-1.4773659	1.5812656	0.1470348	-0.8498113	-0.7145672	-1.5507747	-0.9862481	-2.0531194	0.3202939	-1.0165478	2.1029383	0.2079830	white	FALSE	TRUE
-1.0916925	-0.6661100	-0.0594092	-0.1982502	-0.2864068	0.6464827	0.1460572	-0.4857595	0.5690699	0.7306951	0.2584021	0.2079830	white	FALSE	TRUE
-0.4746151	-0.6053701	0.0782201	0.9787635	-0.6860232	-1.2690750	-0.6147104	0.5213527	-0.1772582	-0.1429263	-0.5800235	0.2079830	white	FALSE	TRUE
-0.8602885	-0.1194510	-0.2658532	0.2431300	-1.0000074	-0.0859364	0.2875954	-0.9392934	-0.4260342	-1.1509511	0.8033787	1.3531236	white	FALSE	TRUE
-1.7087700	-0.8483296	0.5599228	-0.7447208	-1.1712716	-0.5929958	-0.3139418	-1.6662817	0.9422339	0.1258803	1.3064340	0.2079830	white	FALSE	TRUE
-0.9374232	0.4879478	0.6287375	1.4621798	-0.6860232	1.0972022	1.1191322	0.5080134	-0.4882282	0.4618885	-0.6638660	-0.9371575	white	FALSE	TRUE
-1.1688272	-0.7875898	0.0094055	-0.8077751	-0.7431112	0.1394233	0.1106727	-1.4195058	0.6934579	-0.0757247	1.1806702	0.2079830	white	FALSE	TRUE
-1.2459619	-0.8483296	0.2846641	-0.6186122	-0.2293188	-0.8183555	0.1637495	-0.6258214	1.6885620	-0.2773296	-0.4123384	0.2079830	white	FALSE	TRUE
0.1424623	-0.7268499	-0.4034825	-0.8918475	-0.6003911	-0.7056757	-0.3316341	-0.7492093	-0.6126162	-0.8149428	-0.6638660	0.2079830	white	FALSE	TRUE
-0.7831538	0.2449882	0.6975521	-0.6186122	-0.5147590	0.1957633	0.0222113	-1.1260427	0.8800399	0.3946869	0.9291425	1.3531236	white	FALSE	TRUE
-1.0145579	1.2168264	-2.1926638	-0.9759199	-0.6860232	-1.0437153	-0.5970182	-0.6124822	0.1959058	-1.1509511	-1.4184490	-0.9371575	white	FALSE	TRUE
-0.7831538	1.1560865	-2.0550344	-0.9549018	-0.6003911	-1.3817549	-0.6147104	-0.7858922	0.1337118	-1.2181527	-0.8315512	-2.0822980	white	FALSE	TRUE
-0.4746151	0.0020287	0.5599228	0.5584015	-0.2864068	2.1113210	0.9599017	0.0811580	-0.4260342	-0.2101280	-0.8036036	0.2079830	white	FALSE	TRUE
-0.4746151	0.0020287	0.5599228	0.5584015	-0.2864068	2.1113210	0.9599017	0.0811580	-0.4260342	-0.2101280	-0.8036036	0.2079830	white	FALSE	TRUE
-1.7087700	-0.6357400	-0.3346678	1.3255622	-0.7431112	0.1957633	0.0399036	0.2345591	-0.9235862	-0.2101280	-0.9153937	0.2079830	white	FALSE	TRUE
-1.3230966	-0.1194510	-1.2980731	-0.8708294	-0.5433030	0.8155025	0.7122099	-0.9526327	0.2580999	-1.0165478	0.1745595	-0.9371575	white	FALSE	TRUE
-1.7859046	0.7916472	-1.0228145	-0.7447208	-0.6003911	1.6606015	0.5706717	-1.6862905	0.3202939	-1.2181527	0.8452999	0.2079830	white	FALSE	TRUE
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-0.4746151	0.0020287	0.5599228	0.5584015	-0.2864068	2.1113210	0.9599017	0.0811580	-0.4260342	-0.2101280	-0.7896299	0.2079830	white	FALSE	TRUE
-0.7831538	-0.7875898	-0.2658532	0.0539671	-0.8001993	0.8155025	0.0929804	-1.0059896	-0.0528702	3.6875676	1.3902765	1.3531236	white	FALSE	TRUE
-0.7831538	0.4272079	-0.6787412	-0.7447208	-0.9429194	-1.4380948	-1.0570172	-1.8063436	-1.1101682	1.7387198	2.1029383	1.3531236	white	FALSE	TRUE
-0.3203458	-0.7268499	0.2846641	-0.8918475	-0.1151427	0.4211230	0.1991340	-0.4657506	-1.1101682	0.0586786	-1.0830788	-0.9371575	white	FALSE	TRUE
-1.7859046	-0.6357400	-0.3346678	1.3255622	-0.7431112	0.1957633	0.0399036	0.2345591	-0.9235862	-0.2101280	-0.9153937	0.2079830	white	FALSE	TRUE
-0.8602885	0.0020287	-0.1970385	-0.6816665	-0.5718471	-0.3112961	-0.2785573	-1.7729955	-0.9857802	-0.6133379	1.0968276	0.2079830	white	FALSE	TRUE
-1.1688272	-0.7875898	0.0094055	-0.9549018	-0.5147590	0.4211230	0.0929804	-1.3194616	0.1337118	-0.4789346	0.0907169	0.2079830	white	FALSE	TRUE
-0.5517498	-0.6661100	0.4222935	-0.8708294	-0.6860232	-0.0859364	-0.0662500	-0.5724645	0.4446819	0.0586786	-0.6638660	-0.9371575	white	FALSE	TRUE
-0.7831538	-0.7875898	-0.1970385	-0.8077751	-0.4862150	-0.3676361	-0.4200955	-1.1860692	0.3202939	-0.2101280	0.5937723	0.2079830	white	FALSE	TRUE
-0.4746151	-0.1194510	0.2846641	0.5373834	-0.2578628	1.4915817	0.9245172	0.0678187	-0.4260342	-0.4789346	-0.7477086	-0.9371575	white	FALSE	TRUE
-0.5517498	-0.6053701	-0.8851851	-0.8918475	-0.4291269	-0.0295965	-0.0839423	-0.7191961	-1.4211383	-0.4789346	-0.9153937	0.2079830	white	FALSE	TRUE
-1.3230966	-0.3016707	-0.1282238	-0.9128656	-0.9714634	-0.5929958	-0.1016346	-2.0030973	0.7556519	-1.0165478	1.9352531	1.3531236	white	FALSE	TRUE
-0.9374232	-0.7875898	0.4222935	-0.9759199	-1.0285515	-0.4803159	-0.3139418	-1.7629911	0.2580999	-1.4197577	1.0968276	0.2079830	white	FALSE	TRUE

Modeling

Data partition

data_partition <- caret::createDataPartition(df$type,p=0.65,list=F)

train_set <- df[data_partition,]
test_set <- df[-data_partition,]

print(nrow(train_set))

## [1] 4224

print(nrow(test_set))

## [1] 2273

Sigmoid activation function formula

sigmoid <- function(x) {1/(1+exp(-x))}

Neural network formula

nn <- neuralnet(red+white ~. -type,
                hidden = c(3,2,3),
                train_set,
                linear.output = FALSE,
                act.fct = sigmoid,
                learningrate=0.01)

Neural network plot

plot(nn,rep="best",x.entry = 0.25,radius = 0.15,arrow.length = 0.2)

Prediction

pred <- predict(nn, test_set)
table(test_set$type, apply(pred, 1, which.max))

##        
##            1    2
##   red    552    7
##   white    6 1708

nn_pred <- apply(compute(nn,test_set[1:12])$net.result,1,which.max)

head(as.factor(nn_pred))

##  7  8  9 15 21 23 
##  1  1  1  1  1  1 
## Levels: 1 2

levels(test_set$type)

## [1] "red"   "white"

idx <- c("red","white")[nn_pred]

Confusion matrix for the prediction

caret::confusionMatrix(data=as.factor(idx),reference = test_set$type)

## Confusion Matrix and Statistics
## 
##           Reference
## Prediction  red white
##      red    552     6
##      white    7  1708
##                                          
##                Accuracy : 0.9943         
##                  95% CI : (0.9902, 0.997)
##     No Information Rate : 0.7541         
##     P-Value [Acc > NIR] : <2e-16         
##                                          
##                   Kappa : 0.9846         
##                                          
##  Mcnemar's Test P-Value : 1              
##                                          
##             Sensitivity : 0.9875         
##             Specificity : 0.9965         
##          Pos Pred Value : 0.9892         
##          Neg Pred Value : 0.9959         
##              Prevalence : 0.2459         
##          Detection Rate : 0.2429         
##    Detection Prevalence : 0.2455         
##       Balanced Accuracy : 0.9920         
##                                          
##        'Positive' Class : red            
##